Abstract
Situational cognition can help students to construct their knowledge to a great extent. In order to solve the problem of lack of situational cognition in College English teaching, this paper studies the validity of students' situational cognition in College English teaching based on the method of image block gain optimization. First, this paper analyses the general situation of situational cognition capacity in College English teaching in China at present, and puts forward the function of device image in constructing situational cognitive competence in teaching. Then, it divides device image into blocks under pseudo-haze conditions, and proposes the optimization method of block gain. Finally, on the basis of block gain, it makes an empirical test of situational cognitive competence in College English teaching. The empirical results show that image block gain optimization can effectively improve the construction of situational cognition capacity in College English teaching. With the help of this study, some new and useful ideas can be traced for the development of computer science and college English teaching, and also stimulate the further improvement of College English education in China.
Highlights
The surge of information technologies has ushered the higher education in a new era of innovative and technologic information-based teaching model, making sure the college teachers integrate the information technologies represented by multimedia with situational cognition theory when designing pedagogical activities, in order to help students to better self-construct knowledge, and make these activities more efficient and practical.Many experts at home focus more on the study and analysis of English education information
Based on the image block gain optimization, this paper examines the students' situational cognition capacity in college English teaching
We analyze the situational cognition profile in college English teaching at the present stage, and present what's the effect of equipment image on the teaching situation cognition construction; divide the equipment image in pseudo-mist state into blocks, and propose the block gain optimization method; based on the image block gain, we empirically test the situational cognition of college English teaching [12,13,14,15,16,17,18,19,20,21,22,23,24,25]
Summary
The surge of information technologies has ushered the higher education in a new era of innovative and technologic information-based teaching model, making sure the college teachers integrate the information technologies represented by multimedia with situational cognition theory when designing pedagogical activities, in order to help students to better self-construct knowledge, and make these activities more efficient and practical. Some scholars have argued in the study of higher English teaching that the way the situational cognition of students gets improved is to use the multimedia and other technologies for situational creation [1]. Guan et al [2] states that in the higher English education, it is required to concern reasonable application of multimedia technology, and the situation should fit with the teaching content when the inforiJET ‒ Vol 14, No 18, 2019. In view of the importance of the international video in English teaching, Compton [4] proposes that among various methods for creating teaching situations more suitable for native language environment, videos may be more effective for creating situations due to its intuitiveness and audiovisuality. Based on the image block gain optimization, this paper examines the students' situational cognition capacity in college English teaching. We analyze the situational cognition profile in college English teaching at the present stage, and present what's the effect of equipment image on the teaching situation cognition construction; divide the equipment image in pseudo-mist state into blocks, and propose the block gain optimization method; based on the image block gain, we empirically test the situational cognition of college English teaching [12,13,14,15,16,17,18,19,20,21,22,23,24,25]
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